Development of Nanosensor to Detect Mercury and Volatile Organic Vapors
The properties of nanoparticle sensors intended for real- time monitoring of low concentration of elemental mercury (Hg) vapor and volatile organic compounds (VOCs) are presented and discussed. This sensor for mercury vapors is composed of gold (Au) nanoparticles on single-walled carbon nanotubes (SWNTs) networks. Surface topography was determined by scanning electron microscopy (SEM). The electrical resistance of Au-SWNTs networks drastically increased upon exposure to mercury vapor. The experiment result shows that higher deposition amounts of Au nanoparticles on SWNTs lead to higher sensing responses. A detection limit of this senor to vapor mercury concentrations in the parts-per billion (ppb) was seen. Response features of current mercury sensors are discussed concerning sensitivity, reproducibility and regeneration at room temperature (25°C).
Nanosensors made of conducting polypyrrole (PPY) and tin dioxide (SnO2) on SWNTs were tested for the detection of volatile organics such as benzene, methyl ethyl ketone (MEK), hexane and xylene. The greater sensitivity of these two sensors to lower analytes concentrations compared to previous research studies was demonstrated. Experiments were conducted at room temperature, and the response was shown to be fast and highly sensitive to low concentration of VOCs. Using PPY and SnO2 sensors in a sensor array can identify polar and nonpolar analytes. Sensing mechanisms of these two sensors to analytes are discussed in this thesis.
Further work to improve the sensors that were tested was identified. The main challenge of this sensor is that the response and regeneration time is relatively slow at room temperature, especially for Au nanoparticle sensors. Also, with respect to PPY and SnO2 nanosensors, a high reproducibility in the making of sensors is desired. This improvement can help PPY and SnO2 sensors to have consistency. Finally, since nanosensors that can detect VOCs are not very specific, array sensing and numerical methods that can be used to quantify individual compounds in mixture from nanosensors array data are needed.
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